





'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a   	=    logging.get_logger(__name__)

a   	=    {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   for attribute in key.split("""."""   ):
      __SCREAMING_SNAKE_CASE   				=							getattr(__UpperCAmelCase				,						__UpperCAmelCase   )

   if weight_type is not None:
      __SCREAMING_SNAKE_CASE   				=							getattr(__UpperCAmelCase				,						__UpperCAmelCase   ).shape
   else:
      __SCREAMING_SNAKE_CASE   				=							hf_pointer.shape

   assert hf_shape == value.shape, (
       f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
       f""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      __SCREAMING_SNAKE_CASE   				=							value
   elif weight_type == "weight_g":
      __SCREAMING_SNAKE_CASE   				=							value
   elif weight_type == "weight_v":
      __SCREAMING_SNAKE_CASE   				=							value
   elif weight_type == "bias":
      __SCREAMING_SNAKE_CASE   				=							value
   else:
      __SCREAMING_SNAKE_CASE   				=							value

   logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   __SCREAMING_SNAKE_CASE   				=							fairseq_model.state_dict()

   __SCREAMING_SNAKE_CASE   				=							hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __SCREAMING_SNAKE_CASE   				=							False
      if "conv_layers" in name:
         load_conv_layer(
             __UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						hf_model.config.feat_extract_norm == """group"""				,						)
         __SCREAMING_SNAKE_CASE   				=							True
      else:
         for key, mapped_key in MAPPING.items():
            __SCREAMING_SNAKE_CASE   				=							"""hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

            if key in name or (key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0] and not is_finetuned):
               __SCREAMING_SNAKE_CASE   				=							True
               if "*" in mapped_key:
                  __SCREAMING_SNAKE_CASE   				=							name.split(__UpperCAmelCase   )[0].split("""."""   )[-2]
                  __SCREAMING_SNAKE_CASE   				=							mapped_key.replace("""*"""				,						__UpperCAmelCase   )
               if "weight_g" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight_g"""
               elif "weight_v" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight_v"""
               elif "weight" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight"""
               elif "bias" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""bias"""
               else:
                  __SCREAMING_SNAKE_CASE   				=							None
               set_recursively(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
            continue
      if not is_used:
         unused_weights.append(__UpperCAmelCase   )

   logger.warning(f"""Unused weights: {unused_weights}"""   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							full_name.split("""conv_layers."""   )[-1]
   __SCREAMING_SNAKE_CASE   				=							name.split("""."""   )
   __SCREAMING_SNAKE_CASE   				=							int(items[0]   )
   __SCREAMING_SNAKE_CASE   				=							int(items[1]   )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(__UpperCAmelCase   )

@torch.no_grad()
def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						__UpperCAmelCase=True   )    ->       Tuple:

   '''simple docstring'''







   if config_path is not None:
      __SCREAMING_SNAKE_CASE   				=							HubertConfig.from_pretrained(__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							HubertConfig()

   if is_finetuned:
      if dict_path:
         __SCREAMING_SNAKE_CASE   				=							Dictionary.load(__UpperCAmelCase   )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __SCREAMING_SNAKE_CASE   				=							target_dict.pad_index
         __SCREAMING_SNAKE_CASE   				=							target_dict.bos_index
         __SCREAMING_SNAKE_CASE   				=							target_dict.eos_index
         __SCREAMING_SNAKE_CASE   				=							len(target_dict.symbols   )
         __SCREAMING_SNAKE_CASE   				=							os.path.join(__UpperCAmelCase				,						"""vocab.json"""   )
         if not os.path.isdir(__UpperCAmelCase   ):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__UpperCAmelCase   )   )
            return
         os.makedirs(__UpperCAmelCase				,						exist_ok=__UpperCAmelCase   )
         with open(__UpperCAmelCase				,						"""w"""				,						encoding="""utf-8"""   ) as vocab_handle:
            json.dump(target_dict.indices				,						__UpperCAmelCase   )
         __SCREAMING_SNAKE_CASE   				=							WavaVecaCTCTokenizer(
             __UpperCAmelCase				,						unk_token=target_dict.unk_word				,						pad_token=target_dict.pad_word				,						bos_token=target_dict.bos_word				,						eos_token=target_dict.eos_word				,						word_delimiter_token="""|"""				,						do_lower_case=__UpperCAmelCase				,						)
         __SCREAMING_SNAKE_CASE   				=							True if config.feat_extract_norm == """layer""" else False
         __SCREAMING_SNAKE_CASE   				=							WavaVecaFeatureExtractor(
             feature_size=1				,						sampling_rate=16000				,						padding_value=0				,						do_normalize=__UpperCAmelCase				,						return_attention_mask=__UpperCAmelCase				,						)
         __SCREAMING_SNAKE_CASE   				=							WavaVecaProcessor(feature_extractor=__UpperCAmelCase				,						tokenizer=__UpperCAmelCase   )
         processor.save_pretrained(__UpperCAmelCase   )

      __SCREAMING_SNAKE_CASE   				=							HubertForCTC(__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							HubertModel(__UpperCAmelCase   )

   if is_finetuned:
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]				,						arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
   else:
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

   __SCREAMING_SNAKE_CASE   				=							model[0].eval()

   recursively_load_weights(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )

   hf_wavavec.save_pretrained(__UpperCAmelCase   )


if __name__ == "__main__":
    a   	=    argparse.ArgumentParser()
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
    parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument(
        "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    a   	=    parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase    :   Tuple						=			{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   int						=			[
		    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'LukeForEntityClassification',
		    'LukeForEntityPairClassification',
		    'LukeForEntitySpanClassification',
		    'LukeForMultipleChoice',
		    'LukeForQuestionAnswering',
		    'LukeForSequenceClassification',
		    'LukeForTokenClassification',
		    'LukeForMaskedLM',
		    'LukeModel',
		    'LukePreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		lowercase    :   Any						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)